In a recent Azure Architecture blog article, Azure lead engineer Kishorekumar Pattabiraman outlines practical criteria for choosing between skills, sub-agents, and other approaches when building AI systems, emphasizing reusability, simplicity, and long-term maintainability.
According to Pattabiraman, teams often begin with the wrong question, focusing on which model to use. The "first real fork", though, is not the model, but the architecture:
Are you building a skill or a sub-agent? Get that wrong and no model choice will save you. A skill and a sub-agent are two different delivery shapes, and each one fails at the other one's job.
A skill operates within an ongoing conversation: it can read files, ask questions, iterate with the user, and keep a human in the loop throughout the process. A sub-agent, by contrast, takes a single prompt, runs independently to completion, and delivers a final output. Both have their place, depending on the task, says Pattabiraman.
To decide whether to build a skill or a sub-agent, Pattabiraman highlights four key dimensions to consider: the iteration model, voice fidelity, human gate placement, and how frequently the task is repeated.
Among the four dimensions, frequency is the one leading to the clearest divide: a "one-off craft piece leans to a skill, a repeatable batch job to a sub-agent". The other factors require more careful consideration. For example, the choice is rarely between a fully interactive conversation and a simple handoff, so you need to weigh the cost of having a human engaged in an iterative skill-based flow against the risk of forcing that same process into a one-shot response that might need to be corrected each time. For each dimension, Pattabiraman outlines the key trade-offs and common pitfalls to avoid.
The question of when to use a skill versus a sub-agent has also surfaced in discussions across Reddit and Hacker News. One commenter, enthusiast_bob, points out that sub-agents always start clean and don't pollute the context window, whereas a skill always takes the whole conversation into account. Another user, dan-does-ai, emphasizes different trade-offs: a skill "is reusable across multiple agents or conversation flows", whereas "sub-agents make sense when: the step needs genuinely separate context, permissions, or a different knowledge source".
Another important consideration is the need for orchestration arising when using sub-agents. Beyond added complexity, one must also consider the non-determinism introduced by the orchestration layer. For example, Reddit commenter Ashlesha-msft notes that in Copilot Studio:
the planner dynamically decides when to call skills, tools, topics, or sub-agents based on descriptions, context, and recent conversation history. Because of that, a skill might not be invoked on every similar prompt.
As a final perspective from the community, user Vlourenco69 suggests a simple mental model to "make sense of concepts like AI Agents, Sub-Agents, Skills, MCP", where an agent acts as a director, a sub-agent as a manager, a skill as a specialized worker, a tool as a dedicated machine, and MCP as the governance rules or policies of the organization.
Bringing the discussion full circle, Pattabiraman notes that in many cases the dichotomy skill vs. sub-agent is only apparent. In practice, the two models compose cleanly, and a skill can be built on top of a sub-agent when the problem calls for it. In many cases, this layered approach represents the most mature design.